```
S(CI) ≡
S((\fxy.fyx)(\x.x)) ~~>
S(\xy.(\x.x)yx) ~~>
S(\xy.yx) ≡
(\fgx.fx(gx))(\xy.yx) ~~>
\gx.(\xy.yx)x(gx) ~~>
\gx.(gx)x ≡
W
```

Ok, here comes a shift in thinking. Instead of defining combinators as equivalent to certain lambda terms,
we can define combinators by what they do. If we have the I combinator followed by any expression X,
I will take that expression as its argument and return that same expression as the result. In pictures,
IX ~~> X
Thinking of this as a reduction rule, we can perform the following computation
II(IX) ~~> IIX ~~> IX ~~> X
The reduction rule for K is also straightforward:
KXY ~~> X
That is, K throws away its second argument. The reduction rule for S can be constructed by examining
the defining lambda term:
`S ≡ \fgx.fx(gx)`

S takes three arguments, duplicates the third argument, and feeds one copy to the first argument and the second copy to the second argument. So:
SFGX ~~> FX(GX)
If the meaning of a function is nothing more than how it behaves with respect to its arguments,
these reduction rules capture the behavior of the combinators S, K, and I completely.
We can use these rules to compute without resorting to beta reduction.
For instance, we can show how the I combinator is equivalent to a
certain crafty combination of Ss and Ks:
SKKX ~~> KX(KX) ~~> X
So the combinator `SKK` is equivalent to the combinator I.
These reduction rule have the same status with respect to Combinatory
Logic as beta reduction and eta reduction, etc., have with respect to
the lambda calculus: they are purely syntactic rules for transforming
one sequence of symbols (e.g., a redex) into another (a reduced
form). It's worth noting that the reduction rules for Combinatory
Logic are considerably more simple than, say, beta reduction. Since
there are no variables in Combiantory Logic, there is no need to worry
about variable collision.
Combinatory Logic is what you have when you choose a set of combinators and regulate their behavior with a set of reduction rules. As we said, the most common system uses S, K, and I as defined here.
###The equivalence of the untyped lambda calculus and combinatory logic###
We've claimed that Combinatory Logic is equivalent to the lambda
calculus. If that's so, then S, K, and I must be enough to accomplish
any computational task imaginable. Actually, S and K must suffice,
since we've just seen that we can simulate I using only S and K. In
order to get an intuition about what it takes to be Turing complete,
recall our discussion of the lambda calculus in terms of a text editor.
A text editor has the power to transform any arbitrary text into any other arbitrary text. The way it does this is by deleting, copying, and reordering characters. We've already seen that K deletes its second argument, so we have deletion covered. S duplicates and reorders, so we have some reason to hope that S and K are enough to define arbitrary functions.
We've already established that the behavior of combinatory terms can
be perfectly mimicked by lambda terms: just replace each combinator
with its equivalent lambda term, i.e., replace I with `\x.x`, replace
K with `\fxy.x`, and replace S with `\fgx.fx(gx)`. So the behavior of
any combination of combinators in Combinatory Logic can be exactly
reproduced by a lambda term.
How about the other direction? Here is a method for converting an
arbitrary lambda term into an equivalent Combinatory Logic term using
only S, K, and I. Besides the intrinsic beauty of this mapping, and
the importance of what it says about the nature of binding and
computation, it is possible to hear an echo of computing with
continuations in this conversion strategy (though you wouldn't be able
to hear these echos until we've covered a considerable portion of the
rest of the course). In addition, there is a direct linguistic
appliction of this mapping in chapter 17 of Barker and Shan 2014,
where it is used to establish a correpsondence between two natural
language grammars, one of which is based on lambda-like abstraction,
the other of which is based on Combinatory Logic like manipulations.
Assume that for any lambda term T, [T] is the equivalent combinatory logic term. The we can define the [.] mapping as follows:
1. [a] a
2. [(M N)] ([M][N])
3. [\a.a] I
4. [\a.M] KM assumption: a does not occur free in M
5. [\a.(M N)] S[\a.M][\a.N]
6. [\a\b.M] [\a[\b.M]]
It's easy to understand these rules based on what S, K and I do. The first rule says
that variables are mapped to themselves.
The second rule says that the way to translate an application is to translate the
first element and the second element separately.
The third rule should be obvious.
The fourth rule should also be fairly self-evident: since what a lambda term such as `\x.y` does it throw away its first argument and return `y`, that's exactly what the combinatory logic translation should do. And indeed, `Ky` is a function that throws away its argument and returns `y`.
The fifth rule deals with an abstract whose body is an application: the S combinator takes its next argument (which will fill the role of the original variable a) and copies it, feeding one copy to the translation of \a.M, and the other copy to the translation of \a.N. This ensures that any free occurrences of a inside M or N will end up taking on the appropriate value. Finally, the last rule says that if the body of an abstract is itself an abstract, translate the inner abstract first, and then do the outermost. (Since the translation of [\b.M] will not have any lambdas in it, we can be sure that we won't end up applying rule 6 again in an infinite loop.)
[Fussy notes: if the original lambda term has free variables in it, so will the combinatory logic translation. Feel free to worry about this, though you should be confident that it makes sense. You should also convince yourself that if the original lambda term contains no free variables---i.e., is a combinator---then the translation will consist only of S, K, and I (plus parentheses). One other detail: this translation algorithm builds expressions that combine lambdas with combinators. For instance, the translation of our boolean false `\x.\y.y` is `[\x[\y.y]] = [\x.I] = KI`. In the intermediate stage, we have `\x.I`, which mixes combinators in the body of a lambda abstract. It's possible to avoid this if you want to, but it takes some careful thought. See, e.g., Barendregt 1984, page 156.]
[Various, slightly differing translation schemes from combinatorial
logic to the lambda calculus are also possible. These generate
different metatheoretical correspondences between the two
calculii. Consult Hindley and Seldin for details. Also, note that the
combinatorial proof theory needs to be strengthened with axioms beyond
anything we've here described in order to make [M] convertible with
[N] whenever the original lambda-terms M and N are convertible. But
then, we've been a bit cavalier about giving the full set of reduction
rules for the lambda calculus in a similar way. For instance, one
issue is whether reduction rules (in either the lambda calculus or
Combinatory Logic) apply to embedded expressions. Generally, we want
that to happen, but making it happen requires adding explicit axioms.]
Let's check that the translation of the false boolean behaves as expected by feeding it two arbitrary arguments:
KIXY ~~> IY ~~> Y
Throws away the first argument, returns the second argument---yep, it works.
Here's a more elaborate example of the translation. The goal is to establish that combinators can reverse order, so we use the **T** combinator, where `T ≡ \x\y.yx`

:
[\x\y.yx] = [\x[\y.yx]] = [\x.S[\y.y][\y.x]] = [\x.(SI)(Kx)] = S[\x.SI][\x.Kx] = S(K(SI))(S[\x.K][\x.x]) = S(K(SI))(S(KK)I)
We can test this translation by seeing if it behaves like the original lambda term does.
The orginal lambda term lifts its first argument (think of it as reversing the order of its two arguments):
S(K(SI))(S(KK)I) X Y ~~>
(K(SI))X ((S(KK)I) X) Y ~~>
SI ((KK)X (IX)) Y ~~>
SI (KX) Y ~~>
IY (KXY) ~~>
Y X
Voilà: the combinator takes any X and Y as arguments, and returns Y applied to X.
One very nice property of combinatory logic is that there is no need to worry about alphabetic variance, or
variable collision---since there are no (bound) variables, there is no possibility of accidental variable capture,
and so reduction can be performed without any fear of variable collision. We haven't mentioned the intricacies of
alpha equivalence or safe variable substitution, but they are in fact quite intricate. (The best way to gain
an appreciation of that intricacy is to write a program that performs lambda reduction.)
Back to linguistic applications: one consequence of the equivalence between the lambda calculus and combinatory
logic is that anything that can be done by binding variables can just as well be done with combinators.
This has given rise to a style of semantic analysis called Variable Free Semantics (in addition to
Szabolcsi's papers, see, for instance,
Pauline Jacobson's 1999 *Linguistics and Philosophy* paper, "Towards a variable-free Semantics").
Somewhat ironically, reading strings of combinators is so difficult that most practitioners of variable-free semantics
express their meanings using the lambda-calculus rather than combinatory logic; perhaps they should call their
enterprise Free Variable Free Semantics.
A philosophical connection: Quine went through a phase in which he developed a variable free logic.
Quine, Willard. 1960. "Variables explained away" Proceedings of the American Philosophical Society. Volume 104: 343--347. Also in W. V. Quine. 1960. Selected Logical Papers. Random House: New
York. 227--235.
The reason this was important to Quine is similar to the worries that Jim was talking about
in the first class in which using non-referring expressions such as Santa Claus might commit
one to believing in non-existant things. Quine's slogan was that "to be is to be the value of a variable."
What this was supposed to mean is that if and only if an object could serve as the value of some variable, we
are committed to recognizing the existence of that object in our ontology.
Obviously, if there ARE no variables, this slogan has to be rethought.
Quine did not appear to appreciate that Shoenfinkel had already invented combinatory logic, though
he later wrote an introduction to Shoenfinkel's key paper reprinted in Jean
van Heijenoort (ed) 1967 From Frege to Goedel, a source book in mathematical logic, 1879--1931.
Cresswell has also developed a variable-free approach of some philosophical and linguistic interest
in two books in the 1990's.
A final linguistic application: Steedman's Combinatory Categorial Grammar, where the "Combinatory" is
from combinatory logic (see especially his 2000 book, The Syntactic Processs). Steedman attempts to build
a syntax/semantics interface using a small number of combinators, including T ≡ `\xy.yx`, B ≡ `\fxy.f(xy)`,
and our friend S. Steedman used Smullyan's fanciful bird
names for the combinators, Thrush, Bluebird, and Starling.
Many of these combinatory logics, in particular, the SKI system,
are Turing complete. In other words: every computation we know how to describe can be represented in a logical system consisting of only a single primitive operation!
Here's more to read about combinatorial logic.
Surely the most entertaining exposition is Smullyan's [[!wikipedia To_Mock_a_Mockingbird]].
Other sources include
* [[!wikipedia Combinatory logic]] at Wikipedia
* [Combinatory logic](http://plato.stanford.edu/entries/logic-combinatory/) at the Stanford Encyclopedia of Philosophy
* [[!wikipedia SKI combinatory calculus]]
* [[!wikipedia B,C,K,W system]]
* [Chris Barker's Iota and Jot](http://semarch.linguistics.fas.nyu.edu/barker/Iota/)
* Jeroen Fokker, "The Systematic Construction of a One-combinator Basis for Lambda-Terms" Formal Aspects of Computing 4 (1992), pp. 776-780.